def ctpn(self, sess, net, img):
im, im_scale = self.check_img(img)
timer = Timer()
timer.tic()
scores, boxes = test_ctpn(sess, net, im)
timer.toc()
print(('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time,
boxes.shape[0]))
# Visualize detections for each class
CONF_THRESH = 0.9
NMS_THRESH = 0.3
dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep]
keep = np.where(dets[:, 4] >= 0.7)[0]
dets = dets[keep, :]
text_lines = self.connect_proposal(dets[:, :], dets[:, 4],
im.shape[:2])
tmp = im.copy()
text_recs = draw_boxes(tmp, text_lines, caption="im_name", wait=True)
# self.show_results(tmp,im_scale, text_recs, thresh=0.9)
return tmp, text_recs
def text_detect(img):
#ctpn
scale, max_scale = Config.SCALE,Config.MAX_SCALE
img,f = resize_im(img,scale=scale,max_scale=max_scale)
scores, boxes = test_ctpn(sess, net, img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes,scores[:, np.newaxis],img.shape[:2])
text_recs,tmp = draw_boxes(img, boxes, caption='im_name', wait=True,is_display=False)
return text_recs,tmp,img
def text_detect(img):
scores, boxes, img = ctpn(img)
textdetector = TextDetector()
boxes = textdetector.detect(boxes, scores[:, np.newaxis], img.shape[:2])
text_recs, tmp = draw_boxes(img,
boxes,
caption='im_name',
wait=True,
is_display=False)
return text_recs, tmp, img
def __text_detection(self):
scores, self._boxes, self._image = ctpn(self._image, sess, net)
self._boxes = self._text_detector.detect(self._boxes,
scores[:, np.newaxis],
self._image.shape[:2])
self._boxes, self.img_drawed_boxes = draw_boxes(self._image,
self._boxes,
caption='im_name',
wait=True,
is_display=False)
self._boxes, self._image = correct_box(sort_box(self._boxes),
self._image, self._text_process)